Mapping Data: It’s all ‘Storylining’
If you refer to other social science research textbooks, you will often be given a benign and technical definition of storylining in research. Storylining, they say, is only a qualitative and interpretive technique, not something close to the heart of every rhetorical (communication) activity. For example, Bhattacherje (2012) defines it as merely where “categories and relationships are used to explicate and/or refine a story of the observed phenomenon” (p. 115). However, storylining might be better conceived as a research composition. Research writing is a genre of narratives with stricter rules for how narratives connect. These rules make research writing accountable to the truth; using methodological rules to govern how “true” narratives can be confirmed by other researchers (seen to accurately refer to reality), or relegated to the false and unproven ‘myths’ (not referring to reality or lacking data to confirm). Arguing from this perspective of research writing, we encourage you to craft the story which best reflects the truth and significance of your research.
Everyone’s narrative becomes noticeable when it seems connected to our own narrative of what we value: when it says something powerful about yourself or a topic people care about. Why is this? Because a narrative is an account of events which connect to each other, and your audience will remember a good narrative if it connects to them. A good narrative will therefore make all the events within its story meaningful to each other. This means that everything you say attempts to connect back to itself: to tie its point to the research question, and your research question to its contribution (the ‘gap’ it fills). Each piece of information would be abstruse if not for the larger purpose of your paper. For instance, the average income of Uber drivers is more meaningful when it is related to the average income of taxi drivers or wages overall, then shown to be a ignored frame in the debate over Uber, helping the acceptance of a massive change in our transportation economy (and a significant decline in transportation wages). There is a chronology of these events, allowing for cause and consequence to be tidily connected into a story which makes all its information relevant to each other.
Moreover, when we talk of “filling the gap” in the literature, we are essentially pushing you to explain how your story connects to your audience and the narratives they believe in. Why should other researchers care? They care because your narrative appeals to their own or to a larger social reality. Your research story aims to contribute to a collective intellectual project. If you make a prediction that turns out false then the connection of your narrative to that story will weaken. However, if you convince your audience your story belongs, then your narrative will garner attention, acceptance, and integration.
We thus come to our first two rules in storylining your research: (1) ensure internal connections by defining clear categories and their relationships, ensuring that your narrative connects and reacts to itself; and (2) seek external connections, make your narrative connect and react to other contexts in the literature. The first is necessary and the second less so, but truly great research will be able to achieve both exceptionally.
So to begin “storylining,” the goal will be to think hard about showcasing key internal and external connections in your paper. You will want to choose a structure of writing that makes these connections as clear as possible for your reader. For instance, in research a temporal order is deliberately established around the RQ. You begin by introducing the significance of your topic, of what is missing in its investigation, and then pose the research question which you will endeavor to answer in the next 20 or so pages. This makes the structure centered around a promise, a promise that you will mobilize all the foregoing to answer this question. Hence, your structure should aim to elucidate the connection between the development of your evidence and your research question as clearly as possible. The following three tips will help you achieve this more clearly through basic organizational tools.
Organizing Headings: Macro-Structure
The internal connections will come through with strong organization of your data analysis. That is why complex ‘bits’ of data are mobilized under larger themes; so the reader can see the general point you are connecting to the research question in all the smaller bits. Before beginning to write your data analysis, pick three or four sections of your data analysis and really consider how they answer your research question. Then, once that is finished, consider how they relate to each other. If there is any section that you think would be improved by reading the others before it, (ie. that more context would add to your readers’ understanding of its connection to your argument), then place that section at the end. Likewise, try to situate the key data that you think enunciates the most important theme you have discovered near the end of your data analysis section. This way, your reader will be pile-drived with a reminder of just how well your narrative connects with your data just before they move onto the discussion.
Under Headings: Micro-Structure
A microstructure can be quite useful in helping to construct your narrative. Some researchers (e.g. Wilson, 2021) make a list of every paragraph and summarize ‘key themes’ to ensure that a logically related paragraph followed. You should essentially be able to put “paragraph 1, hence paragraph 2” in between all your paragraphs without sounding absurd. In fact, if you have really ordered a narrative which intuitively connects, the reader should never have to go grasping for the connection (i.e. a ‘hence’ is implicit). The connections in your argument should appear so obvious that your reader never realizes that they are being guided by a careful analyst.
Once you establish the temporal order between paragraphs, it is time to relate sentences to those paragraphs, sections, and then the point of the paper. When reading through your analysis and the facts used, ensure that each fact and each analysis connects at least to the point stated at the beginning of the paragraph and then secondly to the sentence before it. It is okay to switch tacts slightly within a paragraph, but as a general rule try to avoid too many “howevers” in a paragraph. Try to start a new paragraph when showcasing a contrasting argument or datapoint so that the ‘short-story’ you advance with each concept is not made so nuanced as to lose its relevance for the larger story.
Finally, you want each individual word to resonate with your paper. Remove words that are not doing the work of accurately describing your narrative, or which inadvertently contradict it. When connecting things back to an abstract term such as “legitimacy,” be sure to use that term tactically when relating your data ( e.g., “Uber’s legitimacy”). Do not force it, but pick key instances near the end of your larger point to relate your argument to legitimacy. Just by resonating that one word with a larger section of your data analysis, you can make the connection between an entire theme and your research question clear to your reader (see Chapter 5 for further advice on academic writing).
Making Connections Beyond Your Research
While what will justify many of your internal connections will depend upon the viewpoint of your reader (e.g., a quantitative sociologist will expect adherence to numerical formulas to ensure the validity and significance of your data), you will also try to resonate your argument with the narratives of your field. This is the most important investigation in your literature review: to understand the narrative of researchers in your field. In reading their papers, play close attention to the conclusions, where they reveal the values that undergird the implications they care about. You will want to know exactly “why they care” about this phenomenon and then apply “why you care” to that sentiment. This way, when you go to justify your research, you will know the key problems and values that are on the mind of fellow researchers.
For instance, in Wilson’s (2021) honours thesis on how Uber garners legitimacy (indicated by their appeal of near century old transportation policy), the key theoretical discussion was “what were the processes that affected public consensus?” Thus, the evidence that mattered was key examples which elucidated how the public comes to consensus on an issue like Uber. When writing the discussion, conclusion, and introduction, he indicated this, and touched upon how his narrative both connected to other narratives and offered something new (see Wilson, 2021).
Likewise, when storylining your data, think about building your narrative towards that connection to the values of other researchers. Draft out the single question which appears to be on the mind of many researchers in your field and then ask how your findings relate to it. Pick an order of presenting these findings so that the reader can clearly see the key points that have developed from your study and then clearly implicate these findings in the larger narrative of researchers in your field (who have likewise spent much time trying to add graceful answers to that same question). Storylining thus encompasses the variety of tools we use to organize data in order to clearly indicate its place in a larger discussion. Chapter 11 (Writing the discussion) will dive into this further, as the discussion is where this external connection is expected to be most forcefully made. However, do not take that as meaning that is the only section of your paper where data is connected to the concerns of your audience. Many of the steps of this external connection should already be established in the data analysis – the discussion will only highlight the connections which are already latent in your data analysis.
Box 9.9 – Storylining Checklist
What story do I want to tell?
- Can I summarize my point in a sentence?
- What evidence does my story help to communicate?
- Does the evidence I am able to present match the evidence that I had to exclude?
- Is it the best evidence for expressing the point I wanted to make?
- Does the evidence sensible build upon itself?
- Is there a quicker way I could summarize and show the significance of my evidence?
- Is the order of its presentation clear (readily understandable) and sensible (easily justifiable)?
What story do others want to hear?
- Is the data (and overall narrative) relevant to my audience?
- Have I made it abundantly clear that my data is relevant to my audience?
- What gap in the larger research narrative of my field does my story address?
- Is there a practical implication to my story?
Bhattacherjee, A. (2012). Social Science Research: Principles, Methods, and Practices https://scholarcommons.usf.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=1002&context=oa_textbooks
Wilson, A. (2022). “Drivers of Dissidence: Vancouver’s Road to Ride-Hailing.” Sojourners.